One of the most common activities prior to using an imaging device, such as a camera, is calibration. Many applications require reasonable estimates of camera parameters, especially those that involve structure and motion recovery.
There is a plethora of prior work on camera calibration. They can be roughly classified as weak, semi-strong and strong calibration techniques.
Strong calibration techniques recover all the camera parameters necessary for correct Euclidean (or scaled Euclidean) structure recovery from images. Many of such techniques require a specific calibration pattern with known exact dimensions. Photogrammetry methods which rely on the use of known calibration points or structures are described by D. C. Brown, “Close-range camera calibration”, Photogrammetric Engineering, 37(8):855–866, August 1971 and R. Y. Tsai, “A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses”, IEEE Journal of Robotics and Automation, RA-3(4):323–344, August 1987. Brown, for example, uses plumb lines to recover distortion parameters. Tsai uses corners of regularly spaced boxes of known dimensions for full camera calibration.
G. Stein, “Accurate internal camera calibration using rotation, with analysis of sources of error”, Fifth International Conference on Computer Vision (ICCV'95), pages 230–236, Cambridge, Mass., June 1995 uses point correspondences between multiple views of a camera that is rotated a full circle to extract intrinsic camera parameters very accurately. There is also proposed self-calibration techniques such as those described by R. I. Hartley “An algorithm for self calibration from several views”, IEEE Computer Society Conference on Computer Vision and Pattern Recognition(CVPR'94), pages 908–912, Seattle, Wash., June 1994, IEEE Computer Society, M. Pollefeys et al., “Self calibration and metric reconstruction in spite varying and unknown internal camera parameters”, International Conference on Computer Vision (ICCV'98), pages 90–95, Bombay, India, January 1998, IEEE Computer Society Press and A. Zisserman et al., “Metric Calibration of a stereo rig”, IEEE Workshop on Representations of Visual Scenes, pages 93–100, Cambridge, Mass., June 1995.
Weak calibration techniques recover a subset of camera parameters that will enable only projective structure recovery through the fundamental matrix. Faugeras, “What can be seen in three dimensions with an uncalibrated stereo rig”, Second European Conference on Computer Vision (ECCV'92), pages 563–578, Santa Margherita Ligure, Italy, May 1992, Springer-Verlag opened the door to this category of techniques. There are numerous other players in the field, such as Hartley, “In defense of the 8-point algorithm”, Fifth International Conference on Computer Vision (ICCV'95), pages 1064–1070, Cambridge, Mass., June 1995, IEEE Computer Society Press and A. Shashua, “Projective structure from uncalibrated images: Structure from motion and recognition”, IEEE transactions on Pattern Analysis and Machine Intelligence, 16(8):7788–790, August 1994.
Semi-strong calibration falls between strong and weak calibration; it allows structures that are close to Euclidean under certain conditions to be recovered. Affine calibration described in J. J. Koenderink et al. “Affine structure from motion”, Journal of the Optical Society of America A, 8:377–385538, 1991 falls into this category. In addition, techniques that assume some subset of camera parameters to be known also fall into this category. They include the technique discussed in H. C. Longuet-Higgins, “A computer algorithm for reconstructing a scene from two projections”, Nature, 293:133–135, 1991 and a technique described by Hartley et al., “Estimation of relative camera positions for uncalibrated cameras, Second European Conference on Computer Vision (ECCV'92) pages 579–587, Santa Margherita, Liguere, Italy, May 1992, Springer-Verlag for recovering camera focal lengths corresponding to two views with the assumption that all other intrinsic camera parameters are known.
The common thread of all these calibration methods is that they require some form of image feature, or registration between multiple images in order to extract camera parameters.